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构建基于机器学习方法的TKA后DVT形成的风险预测模型.

Huanya Li1, Zhicheng He1, Pengcui Li2

  • 1Academy of Medical Sciences, Shanxi Medical University, Shanxi, China.

Medicine
|December 30, 2025
PubMed
概括

这项研究开发了一种物流模型,用于预测膝盖全关节整形术 (TKA) 后深静脉血栓形成 (DVT) 风险. 该模型确定了关键的风险因素,为TKA患者的DVT预防提供了有价值的工具.

关键词:
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科学领域:

  • 整形外科手术 整形外科手术
  • 心血管医学 心血管医学
  • 在医疗保健中的数据科学.

背景情况:

  • 深静脉血栓症 (DVT) 是膝关节全关节整形术 (TKA) 后的一种严重并发症.
  • 准确的风险预测模型对于TKA后有效的DVT预防策略至关重要.

研究的目的:

  • 构建和比较多个机器学习模型,用于预测TKA后DVT风险.
  • 确定与TKA后DVT相关的关键风险因素.
  • 确定临床应用的最佳预测模型.

主要方法:

  • 对1238个TKA患者记录进行了回顾性分析.
  • 使用拉索和博鲁塔来选择特征以确定风险因素.
  • 开发并比较了六种机器学习模型:物流回归,XGBoost,随机森林,AdaBoost,梯度增强决策树和KNN.
  • 使用决策曲线,校准曲线和ROC指标评估模型性能.

主要成果:

  • 确定了11个危险因素,包括贫血,输血量,输血,高血压,D-二次体,血栓时间 (TT),CL,麻醉持续时间,手术持续时间,激活的部分血栓塑时间 (APTT) 和术后疼痛评分.
  • 后勤回归模型展示了最佳的性能和概括能力.
  • SHAP分析显示,较短的APTT,更长的麻醉时间,升高的TT,更高的疼痛分数,减少的D-二次数,减少的CL,增加的输血量,增加的血液损失,更短的手术时间和贫血与增加的DVT风险有关.

结论:

  • 后勤模型是最佳的预测TKA后DVT风险.
  • 识别和管理已识别的风险因素可以帮助减少TKA后的DVT发病率.
  • 这种预测模型为临床医生在评估和预防DVT时提供了有价值的参考.